%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Tognoli_BS2023_2023,
         author = {Tognoli, Marco and Peronato, Giuseppe and K{\"{a}}mpf, J{\'{e}}r{\^{o}}me},
       keywords = {3D City Models, CityGML, Decision Tree Learning, REST API},
       projects = {Idiap},
          month = sep,
          title = {A Machine Learning Model for the Prediction of Building Hourly Heating Demand from CityGML Files: Training Workflow and Deployment as an API},
      booktitle = {Proceedings of Building Simulation 2023: 18th Conference of IBPSA},
           year = {2023},
          pages = {2932 - 2939},
            url = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1570},
            doi = {10.26868/25222708.2023.1570},
       abstract = {We present a workflow for the development and deployment of a data-driven model to estimate the hourly heating demand of buildings.The model is trained and tested using CityGML with the Energy ADE specification and Meteonorm CLI weather-files as the source of the input features.Through an optimization pipeline, an ensemble model including a gradient boosting algorithm presenting a RMSE of 9.5 Wh/m2 of floor area (98.7\% accuracy) and low memory requirements is selected. A short-term predicting model is also developed reporting a RMSE of 4.2 Wh/m2 of floor area (99.7\% accuracy).A web service providing access to a REST API deploying the data-driven model is also developed, allowing for a wide range of applications in third-party tools such as in GIS analysis.}
}